Machine Learning Models and Architectures for Biomedical Signal Processing
eBook - ePub

Machine Learning Models and Architectures for Biomedical Signal Processing

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Machine Learning Models and Architectures for Biomedical Signal Processing

About this book

Machine Learning Models and Architectures for Biomedical Signal Processing presents the fundamental concepts of machine learning techniques for bioinformatics in an interactive way. The book investigates how efficient machine and deep learning models can support high-speed processors with reconfigurable architectures like graphic processing units (GPUs), Field programmable gate arrays (FPGAs), or any hybrid system. This great resource will be of interest to researchers working to increase the efficiency of hardware and architecture design for biomedical signal processing and signal processing techniques. - Covers the hardware architecture implementation of machine learning algorithms - Discusses the software implementation approach and the efficient hardware of machine learning application with FPGA - Presents the major design challenges and research potential in machine learning techniques

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Yes, you can access Machine Learning Models and Architectures for Biomedical Signal Processing by Suman Lata Tripathi,Valentina Emilia Balas,Mufti Mahmud,Soumya Banerjee in PDF and/or ePUB format, as well as other popular books in Computer Science & Artificial Intelligence (AI) & Semantics. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Contents
  6. List of contributors
  7. Preface
  8. Acknowledgments
  9. List of Illustrations
  10. List of Tables
  11. 1 : Recent trends in biomedical informatics
  12. 2 : Biomedical signal processing technique
  13. 3 : Transfer learning-based arrhythmia classification using electrocardiogram
  14. 4 : Exploring machine learning models for biomedical signal processing: a comprehensive review
  15. 5 : Machine learning for audio processing: from feature extraction to model selection
  16. 6 : Enhancing insights: unravelling the potential of preprocessing MRI for artificial intelligence based Alzheimer's disease classification
  17. 7 : Machine learning models for text and image processing
  18. 8 : Assistive technology for neuro-rehabilitation applications using machine learning techniques
  19. 9 : Deep learning architectures in computer vision based medical imaging applications with emerging challenges
  20. 10 : Relevance of artificial intelligence, machine learning, and biomedical devices to healthcare quality and patient outcomes
  21. 11 : Artificial intelligence-based electrocardiogram signal processing applications
  22. 12 : Deep learning approach for the prediction of skin diseases
  23. 13 : Brain–computer interface
  24. 14 : Human-computer interface developments include systems that can decipher enhanced human language and contextual cues while interacting with digital devices
  25. 15 : Brain-computer interfaces for elderly and disabled persons
  26. 16 : Machine learning model implementation with FPGAs
  27. 17 : Smart biomedical devices for smart healthcare
  28. 18 : FPGA implementation for explainable machine learning and deep learning models to real-time problems
  29. 19 : Software applications for biometric informatics
  30. 20 : Smart medical devices: making healthcare more intelligent
  31. 21 : Security modules for biomedical signal processing using Internet of Things
  32. 22 : Artificial intelligence-based diagnostic tools for cardiovascular risk prediction
  33. 23 : Machine learning algorithm approach in risk prediction of liver cancer
  34. Index
  35. A